Improvement and Empirical Study of K-Means Clustering Algorithm Based on Chinese Retrieval

机译：基于汉语检索的K均值聚类算法的改进与实证研究

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K-means is a classical clustering algorithm, which is a hard clustering algorithm. The algorithm has the advantages of simplicity and speed, especially when dealing with large data sets. It can be more efficient and flexible. However, the K-means algorithm also has some shortcomings and defects, such as relying on the initial clustering center to fall into the local optimal solution, easy to love noise points and isolated point effects. The article will optimize and demonstrate the shortcomings and defects of the K-means algorithm.

机译：K-means是一种经典聚类算法，它是一种硬群集算法。该算法具有简单性和速度的优点，特别是在处理大数据集时。它可以更有效灵活。然而，K-Means算法还具有一些缺点和缺陷，例如依靠初始聚类中心落入本地最佳解决方案，易于爱噪声点和孤立的点效应。本文将优化和展示K-Means算法的缺点和缺陷。

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《International Conference on Applications and Techniques in Cyber Intelligence》|2020年|xxxii 1091 pages :|共7页
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作者
Yatao Su;
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原文格式 PDF
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中图分类 TP18-532;
关键词
Chinese search; K-means; Improvement and evidence;

机译：中文搜索;K-means;改进和证据;

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专利

1. Region Based Image Retrieval using k-means and Hierarchical Clustering Algorithms [J] . Dr. K.Sakthivel, R.Abinaya, I.Nivetha, International Journal of Innovative Research in Science, Engineering and Technology . 2014,第1期

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2. Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix and K-Means Clustering Algorithms [J] . Ramamurthy B., K.R. Chandran Journal of computer sciences . 2012,第7期

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3. Content Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering Algorithms | Science Publications [J] . B. Ramamurthy, K. R. Chandran Journal of computer sciences . 2012,第7期

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4. Improvement and Empirical Study of K-Means Clustering Algorithm Based on Chinese Retrieval [C] . Yatao Su International Conference on Applications and Techniques in Cyber Intelligence . 2020

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5. A K-means based watershed imaging segmentation algorithm for banana cluster quality inspection. [D] . Castillo Cepin, Gregorio Alfonso. 2016

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6. Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm Minimum Spanning Tree and Hierarchical Clustering in an Applied Study [O] . Saeedeh Pourahmad, Atefeh Basirat, Amir Rahimi, 2020

机译：初始簇质心的确定是否提高了K-Means聚类算法的性能？应用研究中遗传算法最小生成树和分层聚类的三种混合方法的比较
7. Content Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering Algorithms [O] . K. R. Chandran, B. Ramamurthy 2012

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Improvement and Empirical Study of K-Means Clustering Algorithm Based on Chinese Retrieval

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